---
title: "Data Tidying — In-Class Code"
author: "Data Skills for Psychology"
date: today
format:
  html:
    toc: true
    toc-depth: 2
    theme: cosmo
    code-overflow: wrap
    df-print: kable
---

## Setup

```{r}
#| message: false
library(tidyverse)   # readr, dplyr, tidyr, stringr, etc.
library(readxl)      # read .xlsx files
library(janitor)     # clean_names()
```

Download today's data into your `data/` folder:

-   <https://psyteachr.github.io/ads-v3/data/tidy.csv>
-   <https://psyteachr.github.io/ads-v3/data/untidy.csv>

## Untidy vs Tidy

### An untidy example

```{r}
#| message: true
untidy <- read_csv("data/untidy.csv", show_col_types = FALSE)
untidy
```

### The tidy version

```{r}
#| message: true
tidy_data <- read_csv("data/tidy.csv", show_col_types = FALSE)
tidy_data
```

### And the calculation becomes…

```{r}
tidy_data |>
  mutate(prop_women = women / members) |>
  summarise(
    avg_prop_women = mean(prop_women),
    .by = year
  )
```

## Reshaping Data

### `pivot_longer()`: wide → long

```{r}
long <- pivot_longer(
  data = untidy,
  cols = `1950`:`2025`,
  names_to = "year",
  values_to = "women_members"
)

long
```

### Activity: Sketch a long version

Convert this table to long format. You don't need to write code — just sketch it on paper or in a spreadsheet.

| band               | twitter | instagram |
|:-------------------|:--------|:----------|
| The Beatles        | 3.8     | 3.8       |
| The Rolling Stones | 3.4     | 3.1       |
| One Direction      | 31.3    | 22.8      |

1.  How many rows should the long version have?
2.  What should the new column names be?
3.  **Bonus:** What would the long version look like if we *also* had a `youtube` column?

```{r}
# Your code here (or sketch in a notebook):

```

### `pivot_wider()`: long → wide

```{r}
wide <- pivot_wider(
  data = long,
  id_cols = year,
  names_from = country,
  values_from = women_members
)

wide
```

## One Value Per Cell

### `separate()` to the rescue

```{r}
split_data <- separate(
  data = long,
  col = women_members,            # the column to split
  into = c("women", "members"),   # the new column names
  sep = " / ",                    # how to split
  remove = TRUE,                  # drop the original column?
  convert = TRUE                  # try to fix data types?
)

split_data
```

### Activity: Split the `country` column

The `country` column still combines two pieces of information (`"Argentina (ARG)"`).

1.  Use `separate()` to split it into `country_name` and `country_code`
2.  By default, `separate()` splits at any non-letter, non-number character
3.  You may need the `extra = "drop"` argument to handle the trailing `)`

```{r}
# Your code here:

```

## Pipes in Action

### The same workflow, without pipes

```{r}
untidy <- read_csv("data/untidy.csv", show_col_types = FALSE)

long <- pivot_longer(untidy,
                     cols = `1950`:`2025`,
                     names_to = "year",
                     values_to = "women_members")

split_data <- separate(long,
                       col = women_members,
                       into = c("women", "members"),
                       sep = " / ",
                       convert = TRUE)

tidy_data <- separate(split_data,
                      col = country,
                      into = c("country_name", "country_code"),
                      extra = "drop")
```

### The pipe version

```{r}
tidy_data <- read_csv("data/untidy.csv", show_col_types = FALSE) |>
  pivot_longer(
    cols = `1950`:`2025`,
    names_to = "year",
    values_to = "women_members"
  ) |>
  separate(
    col = women_members,
    into = c("women", "members"),
    sep = " / ",
    convert = TRUE
  ) |>
  separate(
    col = country,
    into = c("country_name", "country_code"),
    extra = "drop"
  )
```